Leukocyte subtype classification with multi-model fusion
Accurate classification of leukocytes is crucial for the diagnosis of hematologic malignancies, particularly leukemia. However, traditional leukocyte classification methods are time-consuming and subject to subjective interpretation by examiners. To address this issue, we aimed to develop a leukocyt...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ding, Yingying [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© International Federation for Medical and Biological Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Medical & biological engineering & computing - Springer Berlin Heidelberg, 1977, 61(2023), 9 vom: 03. Apr., Seite 2305-2316 |
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| Übergeordnetes Werk: |
volume:61 ; year:2023 ; number:9 ; day:03 ; month:04 ; pages:2305-2316 |
| Links: |
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| DOI / URN: |
10.1007/s11517-023-02830-1 |
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| Katalog-ID: |
OLC2144922225 |
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| 520 | |a Accurate classification of leukocytes is crucial for the diagnosis of hematologic malignancies, particularly leukemia. However, traditional leukocyte classification methods are time-consuming and subject to subjective interpretation by examiners. To address this issue, we aimed to develop a leukocyte classification system capable of accurately classifying 11 leukocyte classes, which would aid radiologists in diagnosing leukemia. Our proposed two-stage classification scheme involved a multi-model fusion based on ResNet for rough leukocyte classification, which focused on shape features, followed by fine-grained leukocyte classification using support vector machine for lymphocytes based on texture features. Our dataset consisted of 11,102 microscopic leukocyte images of 11 classes. Our proposed method achieved accurate leukocyte subtype classification with high levels of accuracy, sensitivity, specificity, and precision of 97.03 ± 0.05, 96.76 ± 0.05, 99.65 ± 0.05, and 96.54 ± 0.05, respectively, in the test set. The experimental results demonstrate that the leukocyte classification model based on multi-model fusion can effectively classify 11 leukocyte classes, providing valuable technical support for enhancing the performance of hematology analyzers. Graphical abstract | ||
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| 700 | 1 | |a Feng, Sihao |4 aut | |
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10.1007/s11517-023-02830-1 doi (DE-627)OLC2144922225 (DE-He213)s11517-023-02830-1-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Ding, Yingying verfasserin aut Leukocyte subtype classification with multi-model fusion 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Federation for Medical and Biological Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Accurate classification of leukocytes is crucial for the diagnosis of hematologic malignancies, particularly leukemia. However, traditional leukocyte classification methods are time-consuming and subject to subjective interpretation by examiners. To address this issue, we aimed to develop a leukocyte classification system capable of accurately classifying 11 leukocyte classes, which would aid radiologists in diagnosing leukemia. Our proposed two-stage classification scheme involved a multi-model fusion based on ResNet for rough leukocyte classification, which focused on shape features, followed by fine-grained leukocyte classification using support vector machine for lymphocytes based on texture features. Our dataset consisted of 11,102 microscopic leukocyte images of 11 classes. Our proposed method achieved accurate leukocyte subtype classification with high levels of accuracy, sensitivity, specificity, and precision of 97.03 ± 0.05, 96.76 ± 0.05, 99.65 ± 0.05, and 96.54 ± 0.05, respectively, in the test set. The experimental results demonstrate that the leukocyte classification model based on multi-model fusion can effectively classify 11 leukocyte classes, providing valuable technical support for enhancing the performance of hematology analyzers. Graphical abstract Leukocyte Deep learning Medical image Fine-grained classification Tang, Xuehui aut Zhuang, Yuan aut Mu, Junjie aut Chen, Shuchao aut Liu, Shanshan aut Feng, Sihao aut Chen, Hongbo (orcid)0000-0002-0389-7875 aut Enthalten in Medical & biological engineering & computing Springer Berlin Heidelberg, 1977 61(2023), 9 vom: 03. Apr., Seite 2305-2316 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:61 year:2023 number:9 day:03 month:04 pages:2305-2316 https://doi.org/10.1007/s11517-023-02830-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OPC-MAT GBV_ILN_2018 AR 61 2023 9 03 04 2305-2316 |
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10.1007/s11517-023-02830-1 doi (DE-627)OLC2144922225 (DE-He213)s11517-023-02830-1-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Ding, Yingying verfasserin aut Leukocyte subtype classification with multi-model fusion 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Federation for Medical and Biological Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Accurate classification of leukocytes is crucial for the diagnosis of hematologic malignancies, particularly leukemia. However, traditional leukocyte classification methods are time-consuming and subject to subjective interpretation by examiners. To address this issue, we aimed to develop a leukocyte classification system capable of accurately classifying 11 leukocyte classes, which would aid radiologists in diagnosing leukemia. Our proposed two-stage classification scheme involved a multi-model fusion based on ResNet for rough leukocyte classification, which focused on shape features, followed by fine-grained leukocyte classification using support vector machine for lymphocytes based on texture features. Our dataset consisted of 11,102 microscopic leukocyte images of 11 classes. Our proposed method achieved accurate leukocyte subtype classification with high levels of accuracy, sensitivity, specificity, and precision of 97.03 ± 0.05, 96.76 ± 0.05, 99.65 ± 0.05, and 96.54 ± 0.05, respectively, in the test set. The experimental results demonstrate that the leukocyte classification model based on multi-model fusion can effectively classify 11 leukocyte classes, providing valuable technical support for enhancing the performance of hematology analyzers. Graphical abstract Leukocyte Deep learning Medical image Fine-grained classification Tang, Xuehui aut Zhuang, Yuan aut Mu, Junjie aut Chen, Shuchao aut Liu, Shanshan aut Feng, Sihao aut Chen, Hongbo (orcid)0000-0002-0389-7875 aut Enthalten in Medical & biological engineering & computing Springer Berlin Heidelberg, 1977 61(2023), 9 vom: 03. Apr., Seite 2305-2316 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:61 year:2023 number:9 day:03 month:04 pages:2305-2316 https://doi.org/10.1007/s11517-023-02830-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OPC-MAT GBV_ILN_2018 AR 61 2023 9 03 04 2305-2316 |
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10.1007/s11517-023-02830-1 doi (DE-627)OLC2144922225 (DE-He213)s11517-023-02830-1-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Ding, Yingying verfasserin aut Leukocyte subtype classification with multi-model fusion 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Federation for Medical and Biological Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Accurate classification of leukocytes is crucial for the diagnosis of hematologic malignancies, particularly leukemia. However, traditional leukocyte classification methods are time-consuming and subject to subjective interpretation by examiners. To address this issue, we aimed to develop a leukocyte classification system capable of accurately classifying 11 leukocyte classes, which would aid radiologists in diagnosing leukemia. Our proposed two-stage classification scheme involved a multi-model fusion based on ResNet for rough leukocyte classification, which focused on shape features, followed by fine-grained leukocyte classification using support vector machine for lymphocytes based on texture features. Our dataset consisted of 11,102 microscopic leukocyte images of 11 classes. Our proposed method achieved accurate leukocyte subtype classification with high levels of accuracy, sensitivity, specificity, and precision of 97.03 ± 0.05, 96.76 ± 0.05, 99.65 ± 0.05, and 96.54 ± 0.05, respectively, in the test set. The experimental results demonstrate that the leukocyte classification model based on multi-model fusion can effectively classify 11 leukocyte classes, providing valuable technical support for enhancing the performance of hematology analyzers. Graphical abstract Leukocyte Deep learning Medical image Fine-grained classification Tang, Xuehui aut Zhuang, Yuan aut Mu, Junjie aut Chen, Shuchao aut Liu, Shanshan aut Feng, Sihao aut Chen, Hongbo (orcid)0000-0002-0389-7875 aut Enthalten in Medical & biological engineering & computing Springer Berlin Heidelberg, 1977 61(2023), 9 vom: 03. Apr., Seite 2305-2316 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:61 year:2023 number:9 day:03 month:04 pages:2305-2316 https://doi.org/10.1007/s11517-023-02830-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OPC-MAT GBV_ILN_2018 AR 61 2023 9 03 04 2305-2316 |
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10.1007/s11517-023-02830-1 doi (DE-627)OLC2144922225 (DE-He213)s11517-023-02830-1-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Ding, Yingying verfasserin aut Leukocyte subtype classification with multi-model fusion 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Federation for Medical and Biological Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Accurate classification of leukocytes is crucial for the diagnosis of hematologic malignancies, particularly leukemia. However, traditional leukocyte classification methods are time-consuming and subject to subjective interpretation by examiners. To address this issue, we aimed to develop a leukocyte classification system capable of accurately classifying 11 leukocyte classes, which would aid radiologists in diagnosing leukemia. Our proposed two-stage classification scheme involved a multi-model fusion based on ResNet for rough leukocyte classification, which focused on shape features, followed by fine-grained leukocyte classification using support vector machine for lymphocytes based on texture features. Our dataset consisted of 11,102 microscopic leukocyte images of 11 classes. Our proposed method achieved accurate leukocyte subtype classification with high levels of accuracy, sensitivity, specificity, and precision of 97.03 ± 0.05, 96.76 ± 0.05, 99.65 ± 0.05, and 96.54 ± 0.05, respectively, in the test set. The experimental results demonstrate that the leukocyte classification model based on multi-model fusion can effectively classify 11 leukocyte classes, providing valuable technical support for enhancing the performance of hematology analyzers. Graphical abstract Leukocyte Deep learning Medical image Fine-grained classification Tang, Xuehui aut Zhuang, Yuan aut Mu, Junjie aut Chen, Shuchao aut Liu, Shanshan aut Feng, Sihao aut Chen, Hongbo (orcid)0000-0002-0389-7875 aut Enthalten in Medical & biological engineering & computing Springer Berlin Heidelberg, 1977 61(2023), 9 vom: 03. Apr., Seite 2305-2316 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:61 year:2023 number:9 day:03 month:04 pages:2305-2316 https://doi.org/10.1007/s11517-023-02830-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OPC-MAT GBV_ILN_2018 AR 61 2023 9 03 04 2305-2316 |
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10.1007/s11517-023-02830-1 doi (DE-627)OLC2144922225 (DE-He213)s11517-023-02830-1-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Ding, Yingying verfasserin aut Leukocyte subtype classification with multi-model fusion 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Federation for Medical and Biological Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Accurate classification of leukocytes is crucial for the diagnosis of hematologic malignancies, particularly leukemia. However, traditional leukocyte classification methods are time-consuming and subject to subjective interpretation by examiners. To address this issue, we aimed to develop a leukocyte classification system capable of accurately classifying 11 leukocyte classes, which would aid radiologists in diagnosing leukemia. Our proposed two-stage classification scheme involved a multi-model fusion based on ResNet for rough leukocyte classification, which focused on shape features, followed by fine-grained leukocyte classification using support vector machine for lymphocytes based on texture features. Our dataset consisted of 11,102 microscopic leukocyte images of 11 classes. Our proposed method achieved accurate leukocyte subtype classification with high levels of accuracy, sensitivity, specificity, and precision of 97.03 ± 0.05, 96.76 ± 0.05, 99.65 ± 0.05, and 96.54 ± 0.05, respectively, in the test set. The experimental results demonstrate that the leukocyte classification model based on multi-model fusion can effectively classify 11 leukocyte classes, providing valuable technical support for enhancing the performance of hematology analyzers. Graphical abstract Leukocyte Deep learning Medical image Fine-grained classification Tang, Xuehui aut Zhuang, Yuan aut Mu, Junjie aut Chen, Shuchao aut Liu, Shanshan aut Feng, Sihao aut Chen, Hongbo (orcid)0000-0002-0389-7875 aut Enthalten in Medical & biological engineering & computing Springer Berlin Heidelberg, 1977 61(2023), 9 vom: 03. Apr., Seite 2305-2316 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:61 year:2023 number:9 day:03 month:04 pages:2305-2316 https://doi.org/10.1007/s11517-023-02830-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OPC-MAT GBV_ILN_2018 AR 61 2023 9 03 04 2305-2316 |
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Leukocyte subtype classification with multi-model fusion |
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Leukocyte subtype classification with multi-model fusion |
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Ding, Yingying Tang, Xuehui Zhuang, Yuan Mu, Junjie Chen, Shuchao Liu, Shanshan Feng, Sihao Chen, Hongbo |
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leukocyte subtype classification with multi-model fusion |
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Leukocyte subtype classification with multi-model fusion |
| abstract |
Accurate classification of leukocytes is crucial for the diagnosis of hematologic malignancies, particularly leukemia. However, traditional leukocyte classification methods are time-consuming and subject to subjective interpretation by examiners. To address this issue, we aimed to develop a leukocyte classification system capable of accurately classifying 11 leukocyte classes, which would aid radiologists in diagnosing leukemia. Our proposed two-stage classification scheme involved a multi-model fusion based on ResNet for rough leukocyte classification, which focused on shape features, followed by fine-grained leukocyte classification using support vector machine for lymphocytes based on texture features. Our dataset consisted of 11,102 microscopic leukocyte images of 11 classes. Our proposed method achieved accurate leukocyte subtype classification with high levels of accuracy, sensitivity, specificity, and precision of 97.03 ± 0.05, 96.76 ± 0.05, 99.65 ± 0.05, and 96.54 ± 0.05, respectively, in the test set. The experimental results demonstrate that the leukocyte classification model based on multi-model fusion can effectively classify 11 leukocyte classes, providing valuable technical support for enhancing the performance of hematology analyzers. Graphical abstract © International Federation for Medical and Biological Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Accurate classification of leukocytes is crucial for the diagnosis of hematologic malignancies, particularly leukemia. However, traditional leukocyte classification methods are time-consuming and subject to subjective interpretation by examiners. To address this issue, we aimed to develop a leukocyte classification system capable of accurately classifying 11 leukocyte classes, which would aid radiologists in diagnosing leukemia. Our proposed two-stage classification scheme involved a multi-model fusion based on ResNet for rough leukocyte classification, which focused on shape features, followed by fine-grained leukocyte classification using support vector machine for lymphocytes based on texture features. Our dataset consisted of 11,102 microscopic leukocyte images of 11 classes. Our proposed method achieved accurate leukocyte subtype classification with high levels of accuracy, sensitivity, specificity, and precision of 97.03 ± 0.05, 96.76 ± 0.05, 99.65 ± 0.05, and 96.54 ± 0.05, respectively, in the test set. The experimental results demonstrate that the leukocyte classification model based on multi-model fusion can effectively classify 11 leukocyte classes, providing valuable technical support for enhancing the performance of hematology analyzers. Graphical abstract © International Federation for Medical and Biological Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Accurate classification of leukocytes is crucial for the diagnosis of hematologic malignancies, particularly leukemia. However, traditional leukocyte classification methods are time-consuming and subject to subjective interpretation by examiners. To address this issue, we aimed to develop a leukocyte classification system capable of accurately classifying 11 leukocyte classes, which would aid radiologists in diagnosing leukemia. Our proposed two-stage classification scheme involved a multi-model fusion based on ResNet for rough leukocyte classification, which focused on shape features, followed by fine-grained leukocyte classification using support vector machine for lymphocytes based on texture features. Our dataset consisted of 11,102 microscopic leukocyte images of 11 classes. Our proposed method achieved accurate leukocyte subtype classification with high levels of accuracy, sensitivity, specificity, and precision of 97.03 ± 0.05, 96.76 ± 0.05, 99.65 ± 0.05, and 96.54 ± 0.05, respectively, in the test set. The experimental results demonstrate that the leukocyte classification model based on multi-model fusion can effectively classify 11 leukocyte classes, providing valuable technical support for enhancing the performance of hematology analyzers. Graphical abstract © International Federation for Medical and Biological Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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